dc.contributor.author | Dietze, Stefan | |
dc.contributor.author | Taibi, Davide | |
dc.contributor.author | Yu, Ran | |
dc.contributor.author | Barker, Phil | |
dc.contributor.author | d’Aquin, Mathieu | |
dc.date.accessioned | 2017-10-09T12:29:09Z | |
dc.date.available | 2017-10-09T12:29:09Z | |
dc.date.issued | 2017-04-03 | |
dc.identifier.citation | Dietze, Stefan, Taibi, Davide, Yu, Ran, Barker, Phil, & d'Aquin, Mathieu. (2017). Analysing and Improving Embedded Markup of Learning Resources on the Web. Paper presented at the Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia. | en_IE |
dc.identifier.uri | http://hdl.handle.net/10379/6893 | |
dc.description.abstract | Web-scale reuse and interoperability of learning resources have
been major concerns for the technology-enhanced learning
community. While work in this area traditionally focused on
learning resource metadata, provided through learning resource
repositories, the recent emergence of structured entity markup on
the Web through standards such as RDFa and Microdata and
initiatives such as schema.org, has provided new forms of entitycentric
knowledge, which is so far under-investigated and hardly
exploited. The Learning Resource Metadata Initiative (LRMI)
provides a vocabulary for annotating learning resources through
schema.org terms. Although recent studies have shown markup
adoption by approximately 30% of all Web pages, understanding
of the scope, distribution and quality of learning resources markup
is limited. We provide the first public corpus of LRMI extracted
from a representative Web crawl together with an analysis of
LRMI adoption on the Web, with the goal to inform data
consumers as well as future vocabulary refinements through a
thorough understanding of the use as well as misuse of LRMI
vocabulary terms. While errors and schema misuse are frequent,
we also discuss a set of simple heuristics which significantly
improve the accuracy of markup, a prerequisite for reusing
learning resource metadata sourced from markup. | en_IE |
dc.description.sponsorship | This work has been partially supported by the H2020 programme
of the European Union under grant agreement No 687916 – AFEL
project (http://afel-project.eu/) and the COST Action KEYSTONE
(IC1302). | en_IE |
dc.format | application/pdf | en_IE |
dc.language.iso | en | en_IE |
dc.publisher | ACM | en_IE |
dc.relation.ispartof | Proceedings of the 26th International Conference on World Wide Web Companion | en |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Ireland | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/3.0/ie/ | |
dc.subject | LRMI | en_IE |
dc.subject | Schema.org | en_IE |
dc.subject | Web markup | en_IE |
dc.subject | Learning resources | en_IE |
dc.title | Analysing and improving embedded markup of learning resources on the web | en_IE |
dc.type | Conference Paper | en_IE |
dc.date.updated | 2017-10-05T14:44:06Z | |
dc.identifier.doi | 10.1145/3041021.3054160 | |
dc.local.publishedsource | https://dl.acm.org/citation.cfm?id=3054160 | en_IE |
dc.description.peer-reviewed | non-peer-reviewed | |
dc.internal.rssid | 13252791 | |
dc.local.contact | Mathieu D'Aquin. Email: mathieu.daquin@nuigalway.ie | |
dc.local.copyrightchecked | Yes | |
dcterms.project | info:eu-repo/grantAgreement/EC/H2020::RIA/687916/EU/AFEL - Analytics For Everyday Learning/AFEL | |
nui.item.downloads | 9705 | |